import torchvision
from torch import nn
from torch.nn import Sequential, Conv2d, MaxPool2d, Flatten, Linear, CrossEntropyLoss
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

from model import *

# 准备数据集

train_data = torchvision.datasets.CIFAR10(root='./dataset', train=True, transform=torchvision.transforms.ToTensor(), download=True)
test_data = torchvision.datasets.CIFAR10(root='./dataset', train=False, transform=torchvision.transforms.ToTensor(), download=True)

# 数据集的长度
train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练数据集的长度:{}, 测试数据集的长度：{}".format(train_data_size, test_data_size))

# 利用DataLoader 加载数据集
train_data_loader = DataLoader(train_data, batch_size=64)
test_data_loader = DataLoader(test_data, batch_size=64)

# 导入模型
Model = module()

#损失函数
loss_fn = CrossEntropyLoss()

# 优化器
learning_rate = 1e-2
optimizer = torch.optim.SGD(Model.parameters(), lr=learning_rate)

# 设置训练网络的一些参数
# 记录训练的次数
total_train_step = 0

# 记录测试的次数

total_test_step = 0

# 添加tensorboard
writer = SummaryWriter('./train_logs')

# 训练的轮数
epoch = 10
for i in range(epoch):
    print("------第{}轮训练开始------".format(i+1))
    # 训练开始
    Model.train()
    for data in train_data_loader:
        imgs, targets = data
        output = Model(imgs)
        loss = loss_fn(output, targets)
        # 优化器调优
        # 梯度清零
        optimizer.zero_grad()
        # 反向传播
        loss.backward()
        optimizer.step()

        total_train_step += 1
        if total_train_step % 100 == 0:
            print("训练次数:{}, Loss:{}".format(total_train_step, loss.item()))
            writer.add_scalar('train_loss', loss.item(), total_train_step)

    # 测试步骤开始
    Model.eval()
    total_test_loss = 0
    total_accuracy = 0

    with torch.no_grad():
        for data in test_data_loader:
            imgs, targets = data
            outputs = Model(imgs)
            loss = loss_fn(outputs, targets)
            total_test_loss = total_test_loss + loss.item()
            accuracy = (outputs.argmax(1) + targets).sum()
            total_accuracy = accuracy + total_accuracy

    print("整体测试集上的Loss：{}".format(total_test_loss))
    print("整体测集上的正确率：{}".format(total_accuracy/test_data_size))
    writer.add_scalar('test_loss', total_test_loss, total_test_step)
    writer.add_scalar('test_accuracy', total_accuracy/test_data_size, total_test_step)
    total_test_step += 1

    torch.save(Model, './train_module/Module_{}.pth'.format(i))
    # 官方推荐 模型保存方式
    # torch.save(Model.state_dict(), './train_module/Module_{}.pth'.format(i))
    print('模型已保存')

writer.close()
